band_model_uncertainties = 2.5 * np.log10(filter_defs[:, 2].astype(float))
phot_band_cols = filter_defs[:, 3].astype(str)
e_phot_band_cols = filter_defs[:, 4].astype(str)
# Scale factor for synthetic colour residuals
phot_scale_fac = 20
# Stars to treat as equal mass binaries whose single star flux should be halved
unresolved_equal_mass_binary_list = ["5724250571514167424"]
unresolved_equal_mass_binary_mag_diff = 0.75
# Literature information (including photometry)
info_cat_path = "data/{}_info.tsv".format(cat_label)
info_cat = utils.load_info_cat(
info_cat_path,
in_paper=True,
only_observed=True,
unresolved_equal_mass_binary_list=unresolved_equal_mass_binary_list,
)
only_fit_info_cat_stars = True
# Initialise settings for each band
band_settings_b = {
"inst_res_pow": 3000,
"wl_min": 3500,
"wl_max": 5700,
"n_px": 2858,
"wl_per_px": 0.77,
"wl_broadening": 0.77,
"arm": "b",
"grid": "B3000",
"""Script to generate all paper figures and tables
"""
import plumage.plotting as pplt
import plumage.utils as utils
import plumage.spectra as spec
import plumage.paper as paper
spec_path = "spectra"
# Import literature info for both standards and TESS targets
std_info = utils.load_info_cat("data/std_info.tsv")
tic_info = utils.load_info_cat("data/tess_info.tsv")
# Load results tables for both standards and TESS targets
obs_std = utils.load_fits_obs_table("std", path="spectra")
obs_tess = utils.load_fits_obs_table("tess", path="spectra")
# Load spectra and results for standards
spec_std_b, spec_std_r, obs_std = utils.load_fits("std", path=spec_path)
bad_px_masks_std_b = utils.load_fits_image_hdu("bad_px", "std", arm="b")
bad_px_masks_std_r = utils.load_fits_image_hdu("bad_px", "std", arm="r")
synth_std_b = utils.load_fits_image_hdu("synth", "std", arm="b")
synth_std_r = utils.load_fits_image_hdu("synth", "std", arm="r")
synth_std_lit_b = utils.load_fits_image_hdu("synth_lit", "std", arm="b")
synth_std_lit_r = utils.load_fits_image_hdu("synth_lit", "std", arm="r")
# Load spectra and results for TESS targets
spec_tess_b, spec_tess_r, obs_tess = utils.load_fits("tess", path=spec_path)
bad_px_masks_tess_b = utils.load_fits_image_hdu("bad_px", "tess", arm="b")
bad_px_masks_tess_r = utils.load_fits_image_hdu("bad_px", "tess", arm="r")
synth_tess_b = utils.load_fits_image_hdu("synth", "tess", arm="b")
import plumage.plotting as pplt
import astropy.constants as const
# Timing
start_time = datetime.now()
# -----------------------------------------------------------------------------
# Setup
# -----------------------------------------------------------------------------
label = "tess"
spec_path = "spectra"
# Load in literature info for our stars
tic_info = utils.load_info_cat(
remove_fp=True,
only_observed=True,
use_mann_code_for_masses=True,
).reset_index()
tic_info.set_index("TIC", inplace=True)
# Load NASA ExoFOP info on TOIs
toi_info = utils.load_exofop_toi_cat(do_ctoi_merge=True)
# Load in info on observations and fitting results
observations = utils.load_fits_table(
"OBS_TAB",
label,
path=spec_path,
)
# Choose binning, and import light curves to fit with
# Import
spec_std_b, spec_std_r, obs_std = utils.load_fits("std_fluxed", path=spec_path)
#synth_std_b = utils.load_fits_image_hdu("synth", "std", arm="b")
#synth_std_r = utils.load_fits_image_hdu("synth", "std", arm="r")
synth_std_lit_b = utils.load_fits_image_hdu("synth_lit", "std_fluxed", arm="b")
synth_std_lit_r = utils.load_fits_image_hdu("synth_lit", "std_fluxed", arm="r")
# Mask to only those with literature synthetic spectra
mask = ~np.isnan(np.sum(synth_std_lit_b, axis=1))
spec_std_b = spec_std_b[mask]
spec_std_r = spec_std_r[mask]
obs_std = obs_std[mask]
synth_std_lit_b = synth_std_lit_b[mask]
synth_std_lit_r = synth_std_lit_r[mask]
std_info = utils.load_info_cat("data/std_info.tsv")
obs_std = obs_std.join(std_info, "source_id", how="inner", rsuffix="_")
star_dict = {
134:("Teff~3192, logg~5.08, [FeH]~-0.02, Gl 447 (3796072592206250624)",
"phoenix/lte032-5.0-0.0a+0.0.BT-Settl.7",
"phoenix/lte032.0-5.0-0.0a+0.0.BT-Settl.spec.7"),
97:("Teff~3261 logg~4.95, [Fe/H]~0.16, GJ 3379 (3316364602541746048)",
"",
"",),
4:("Teff~3298 logg~4.83, [Fe/H]~0.17, Gl 876 (2603090003484152064)",
"",
"",),
23:("Teff~3530, logg~4.78, [FeH]~+0.35, Gl 849 (2627117287488522240)",
"phoenix/lte035-5.0+0.3a+0.0.BT-Settl.7",
"""Script to generate synthetic spectra at the literature values for stellar
standards.
"""
import numpy as np
import plumage.utils as utils
import plumage.synthetic as synth
import plumage.spectra as spec
from astropy import constants as const
from scipy.interpolate import InterpolatedUnivariateSpline as ius
label = "std"
spec_path = "spectra"
std_info = utils.load_info_cat("data/std_info.tsv", in_paper=True)
spectra_b, spectra_r, observations = utils.load_fits(label, path=spec_path)
# Initlise empty arrays to hold synthetic spectra at lit params
all_synth_lit_b = []
all_synth_lit_r = []
# Intitialise wavelength scale values
band_settings_b = {
"inst_res_pow":3000,
"wl_min":3500,
"wl_max":5700,
"n_px":2858,
"wl_per_px":0.77,
"wl_broadening":0.77,
"arm":"b",
"grid":"B3000",
}
"""Script to download and save complete lightcurves for all TOIs
"""
import numpy as np
import plumage.utils as utils
import plumage.transits as transit
# Binning factor. Note that we're assuming a minimum cadence of 2 mins, so
# extended mission data will get binned to at least that level
bin_fac = 4
# Import catalogue of TESS info
tess_info = utils.load_info_cat(
remove_fp=True,
only_observed=True,
use_mann_code_for_masses=False,
do_extinction_correction=False,)
# Load ExoFOP info
efi = utils.load_exofop_toi_cat()
all_lightcurves = []
unmatched_tics = []
sector_list = []
for star_i, (source_id, star) in enumerate(tess_info.iterrows()):
print("\nDownloading lightcurve {}/{} for TIC {}".format(
star_i+1, len(tess_info), star["TIC"]))
# Download light curve for current star
try:
lc, sectors = transit.download_lc_all_sectors(
# Polynomial order of the fitted offset function
offset_poly_order = 1
# Whether to apply an additional linear correction to the residuals
apply_correction = True
# Whether to plot remaining residual trend
plot_resid_trend = False
# -----------------------------------------------------------------------------
# Import and setup
# -----------------------------------------------------------------------------
# Import CPM info
cpm_info = utils.load_info_cat(
"data/cpm_info.tsv",
clean=False,
allow_alt_plx=True,
use_mann_code_for_masses=False,
do_extinction_correction=False,)
# Only keep reliable pairs
cpm_info = cpm_info[cpm_info["included"] == "yes"]
if use_only_aaa_2mass:
cpm_info = cpm_info[cpm_info["Qflg"] == "AAA"]
if rule_out_gaia_dups:
cpm_info = cpm_info[cpm_info["dup"] == 0]
if apply_ruwe_cut:
cpm_info = cpm_info[cpm_info["ruwe"] < 1.4]
def make_table_ld_coeff(
break_row=64,
label="tess",
info_cat=None,
):
"""Make the final results table to display the angular diameters and
derived fundamental parameters.
Parameters
----------
break_row: int
Which row to break table 1 at and start table 2.
"""
# Load in observations, TIC, and TOI info
observations = utils.load_fits_table("OBS_TAB", label, path="spectra")
if info_cat is None:
info_cat = utils.load_info_cat(
remove_fp=True,
in_paper=True,
only_observed=True,
use_mann_code_for_masses=True,
)
if label == "tess":
id_col = "TIC"
else:
id_col = "Gaia DR2"
comb_info = observations.join(info_cat,
"source_id",
rsuffix="_info",
how="inner")
comb_info.sort_values("teff_synth", inplace=True)
cols = OrderedDict([
(id_col, ""),
(r"$a_1$", ""),
(r"$a_2$", ""),
(r"$a_3$", ""),
(r"$a_4$", ""),
])
header = []
header_1 = []
header_2 = []
table_rows = []
footer = []
notes = []
# Construct the header of the table
header.append("\\begin{table}")
header.append("\\centering")
#header.append("\\label{tab:ld_coeff}")
header.append("\\begin{tabular}{%s}" % ("c" * len(cols)))
header.append("\hline")
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.keys()))
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.values()))
header.append("\hline")
# Now add the separate info for the two tables
header_1 = header.copy()
caption = ("Nonlinear limb darkening coefficients from "
"\citet{claret_limb_2017}")
header_1.insert(2, "\\caption{{{}}}".format(caption))
header_1.insert(3, "\\label{tab:ld_coeff}")
header_2 = header.copy()
header_2.insert(2, "\\contcaption{Limb darkening coefficients}")
# Populate the table for every science target
for star_i, star in comb_info.iterrows():
table_row = ""
# Step through column by column
if label == "tess":
table_row += "%s & " % star["TIC"]
else:
table_row += "%s & " % star.name
# Coefficients
table_row += r"{:0.3f} & ".format(star["ldc_a1"])
table_row += r"{:0.3f} & ".format(star["ldc_a2"])
table_row += r"{:0.3f} & ".format(star["ldc_a3"])
table_row += r"{:0.3f} ".format(star["ldc_a4"])
table_rows.append(table_row + r"\\")
# Finish the table
footer.append("\hline")
footer.append("\end{tabular}")
footer.append("\\end{table}")
# Write the table/s
break_rows = np.arange(break_row, len(comb_info), break_row)
low_row = 0
for table_i, break_row in enumerate(break_rows):
if table_i == 0:
header = header_1
else:
header = header_2
table_x = header + table_rows[low_row:break_row] + footer + notes
np.savetxt("paper/table_ldc_{}_{:0.0f}.tex".format(label, table_i),
table_x,
fmt="%s")
low_row = break_row
# Do final part table
if low_row < len(comb_info):
table_i += 1
table_x = header_2 + table_rows[low_row:] + footer + notes
np.savetxt("paper/table_ldc_{}_{:0.0f}.tex".format(label, table_i),
table_x,
fmt="%s")
def make_table_targets(
break_row=45,
tess_info=None,
):
"""Make the LaTeX table to summarise the target information.
Parameters
----------
break_row: int
Which row to break table 1 at and start table 2.
"""
# Load in the TESS target info
if tess_info is None:
tess_info = utils.load_info_cat(remove_fp=True,
only_observed=True,
in_paper=True)
tess_info.sort_values("G_mag", inplace=True)
cols = OrderedDict([("TOI$^a$", ""), ("TIC$^b$", ""), ("2MASS$^c$", ""),
("Gaia DR2$^d$", ""), ("RA$^d$", "(hh mm ss.ss)"),
("DEC$^d$", "(dd mm ss.ss)"), ("$G^d$", "(mag)"),
("${B_p-R_p}^d$", "(mag)"), ("Plx$^d$", "(mas)"),
("ruwe$^d$", ""), ("E($B-V$)", ""),
(r"N$_{\rm pc}^e$", "")])
header = []
header_1 = []
header_2 = []
table_rows = []
footer = []
notes = []
# Construct the header of the table
header.append("\\begin{landscape}")
header.append("\\begin{table}")
header.append("\\centering")
header.append("\\begin{tabular}{%s}" % ("c" * len(cols)))
header.append("\hline")
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.keys()))
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.values()))
header.append("\hline")
# Now add the separate info for the two tables
header_1 = header.copy()
header_1.insert(3, "\\caption{Science targets}")
header_1.insert(4, "\\label{tab:science_targets}")
header_2 = header.copy()
header_2.insert(3, "\\contcaption{Science targets}")
# Populate the table for every science target
for star_i, star in tess_info.iterrows():
table_row = ""
# Only continue if both observed and not FP
if star["observed"] == "yes" and star["TFOPWG Disposition"] == "FP":
continue
# Format RA and DEC
ra_hr = np.floor(star["ra"] / 15)
ra_min = np.floor((star["ra"] / 15 - ra_hr) * 60)
ra_sec = ((star["ra"] / 15 - ra_hr) * 60 - ra_min) * 60
ra = "{:02.0f} {:02.0f} {:05.2f}".format(ra_hr, ra_min, ra_sec)
dec_deg = np.floor(star["dec"])
dec_min = np.floor((star["dec"] - dec_deg) * 60)
dec_sec = ((star["dec"] - dec_deg) * 60 - dec_min) * 60
dec = "{:+02.0f} {:02.0f} {:05.2f}".format(dec_deg, dec_min, dec_sec)
# Step through column by column
table_row += "%s & " % int(star["TOI"])
table_row += "%s & " % star["TIC"]
#table_row += "%s & " % star["TOI"]
table_row += "%s & " % star["2mass"]
table_row += "%s & " % star_i
table_row += "%s & " % ra
table_row += "%s & " % dec
table_row += "%0.2f & " % star["G_mag"]
table_row += "%0.2f & " % star["Bp-Rp"]
table_row += r"%0.2f $\pm$ %0.2f & " % (star["plx"], star["e_plx"])
table_row += "%0.1f & " % star["ruwe"]
table_row += "%0.2f & " % star["ebv"]
table_row += "%i " % star["n_pc"]
# Replace any nans with '-'
table_rows.append(table_row.replace("nan", "-") + r"\\")
# Finish the table
footer.append("\\hline")
footer.append("\\end{tabular}")
footer_2 = footer.copy()
# Add notes section with references
notes.append("\\begin{minipage}{\linewidth}")
notes.append("\\vspace{0.1cm}")
notes.append("\\textbf{Notes:} $^a$ TESS Object of Interest ID, "
"$^b$ TESS Input Catalogue ID "
"\citep{stassun_tess_2018, stassun_revised_2019},"
"$^c$2MASS \citep{skrutskie_two_2006}, "
"$^c$Gaia \citep{brown_gaia_2018} - "
" note that Gaia parallaxes listed here have been "
"corrected for the zeropoint offset, "
"$^d$Number of candidate planets, NASA Exoplanet Follow-up "
"Observing Program for TESS \\\\")
notes.append("\\end{minipage}")
notes.append("\\end{table}")
notes.append("\\end{landscape}")
footer_2.append("\\end{table}")
footer_2.append("\\end{landscape}")
# Write the tables
table_1 = header_1 + table_rows[:break_row] + footer + notes
table_2 = header_2 + table_rows[break_row:] + footer_2
# Write the table
np.savetxt("paper/table_targets_1.tex", table_1, fmt="%s")
np.savetxt("paper/table_targets_2.tex", table_2, fmt="%s")
def make_table_final_results(
break_row=45,
label="tess",
logg_col="logg_synth",
exp_scale=-12,
info_cat=None,
do_activity_crossmatch=False,
):
"""Make the final results table to display the angular diameters and
derived fundamental parameters.
Parameters
----------
break_row: int
Which row to break table 1 at and start table 2.
"""
# Load in observations, TIC, and TOI info
observations = utils.load_fits_table("OBS_TAB", label, path="spectra")
# Crossmatch with activity
if do_activity_crossmatch:
observations = utils.merge_activity_table_with_obs(
observations, label, fix_missing_source_id=True)
if info_cat is None:
info_cat = utils.load_info_cat(
remove_fp=True,
in_paper=True,
only_observed=True,
use_mann_code_for_masses=True,
)
if label == "tess":
id_col = "TIC"
caption = "Final results for TESS candidate exoplanet hosts"
else:
id_col = "Gaia DR2"
caption = "Final results for cool dwarf standards"
comb_info = observations.join(info_cat,
"source_id",
rsuffix="_info",
how="inner")
comb_info.sort_values("teff_synth", inplace=True)
cols = OrderedDict([
("TOI", ""),
(id_col, ""),
(r"$T_{\rm eff}$", "(K)"),
(r"$\log g$", ""),
(r"[Fe/H]", ""),
(r"$M$", "($M_\odot$)"),
(r"$R$", "($R_\odot$)"),
(r"$m_{\rm bol}$", ""),
(r"$f_{\rm bol}$",
r"{{\footnotesize(10$^{%i}\,$ergs s$^{-1}$ cm $^{-2}$)}}" %
exp_scale),
#(r"$L$", "($L_\odot$)"),
(r"EW(H$\alpha$)", r"\SI{}{\angstrom}"),
(r"$\log R^{'}_{\rm HK}$", ""),
])
# Pop activity keys if making standards
if label != "tess":
cols.pop("TOI")
cols.pop(r"EW(H$\alpha$)")
cols.pop(r"$\log R^{'}_{\rm HK}$")
header = []
header_1 = []
header_2 = []
table_rows = []
footer = []
notes = []
# Construct the header of the table
header.append("\\begin{table*}")
header.append("\\centering")
header.append("\\begin{tabular}{%s}" % ("c" * len(cols)))
header.append("\hline")
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.keys()))
header.append((("%s & " * len(cols))[:-2] + r"\\") % tuple(cols.values()))
header.append("\hline")
# Now add the separate info for the two tables
header_1 = header.copy()
header_1.insert(2, "\\label{{tab:final_results_{}}}".format(label))
header_1.insert(2, "\\caption{{{}}}".format(caption))
header_2 = header.copy()
header_2.insert(2, "\\contcaption{{{}}}".format(caption))
# Populate the table for every science target
for star_i, star in comb_info.iterrows():
# Check to make sure this star has results
if np.isnan(star["teff_synth"]):
print("Skipping", star_i)
continue
table_row = ""
# Step through column by column
if label == "tess":
table_row += "%s & " % int(star["TOI"])
table_row += "%s & " % star["TIC"]
else:
table_row += "%s & " % star.name
# Temperature
table_row += r"{:0.0f} $\pm$ {:0.0f} & ".format(
star["teff_synth"], star["e_teff_synth"])
table_row += r"{:0.2f} $\pm$ {:0.2f} &".format(
star[logg_col], star["e_{}".format(logg_col)])
# Photometric [Fe/H]
if np.isnan(star["phot_feh"]):
table_row += "- &"
else:
table_row += r"{:0.2f} &".format(star["phot_feh"])
# Mass and radius
table_row += r"{:0.3f} $\pm$ {:0.3f} &".format(star["mass_m19"],
star["e_mass_m19"])
table_row += r"{:0.3f} $\pm$ {:0.3f} &".format(star["radius"],
star["e_radius"])
# Bolometric magnitude
table_row += r"{:0.2f} $\pm$ {:0.2f} &".format(star["mbol_synth"],
star["e_mbol_synth"])
# For fbol representation, split mantissa and exponent
table_row += r"{:5.1f} $\pm$ {:0.1f} &".format(
star["fbol"] / 10**exp_scale, star["e_fbol"] / 10**exp_scale)
# Luminosity
#table_row += r"{:0.3f} $\pm$ {:0.3f} & ".format(
#star["L_star"], star["e_L_star"])
# H alpha and logR`HK
if do_activity_crossmatch and label == "tess":
table_row += r"{:0.2f} & ".format(star["EW(Ha)"])
table_row += r"{:0.2f} ".format(star["logR'HK"])
elif label == "tess":
table_row += r"{:0.2f} & ".format(np.nan)
table_row += r"{:0.2f} ".format(np.nan)
# Not writing activity columns, remove &
else:
table_row = table_row[:-2]
# Get rid of nans, append
table_rows.append(table_row.replace("nan", "-") + r"\\")
# Finish the table
footer.append("\hline")
footer.append("\end{tabular}")
footer.append("\\end{table*}")
# Write the table/s
break_rows = np.arange(break_row, len(comb_info), break_row)
low_row = 0
for table_i, break_row in enumerate(break_rows):
if table_i == 0:
header = header_1
else:
header = header_2
table_x = header + table_rows[low_row:break_row] + footer + notes
np.savetxt("paper/table_final_results_{}_{:0.0f}.tex".format(
label, table_i),
table_x,
fmt="%s")
low_row = break_row
# Do final part table
if low_row < len(comb_info):
table_i += 1
table_x = header_2 + table_rows[low_row:] + footer + notes
np.savetxt("paper/table_final_results_{}_{:0.0f}.tex".format(
label, table_i),
table_x,
fmt="%s")
[1.15, 1.05, 0, 0, 0, 2, 1.2, 1.05, 1.05, 1.05])
band_model_uncertainties[~np.isfinite(band_model_uncertainties)] = 0
# Bp, Rp, J, H, K, v, g, r, i, z
phot_mask = np.array([0, 1, 1, 1, 1, 0, 0, 1, 1, 1]).astype(bool)
filters = filters[phot_mask]
phot_band_cols = phot_band_cols[phot_mask]
e_phot_band_cols = e_phot_band_cols[phot_mask]
band_model_uncertainties = band_model_uncertainties[phot_mask]
# Scale factor for synthetic colour residuals
phot_scale_fac = 1
# Literature information (including photometry)
info_cat_path = "data/{}_info.tsv".format(label)
info_cat = utils.load_info_cat(info_cat_path, only_observed=True)
skymapper_phot = pd.read_csv("data/rains_all_gaia_ids_matchfinal.csv",
sep=",",
dtype={"source_id": str},
header=0)
skymapper_phot.set_index("source_id", inplace=True)
info_cat = info_cat.join(skymapper_phot, "source_id", how="inner", rsuffix="_")
# Mask for testing
#mask = np.isfinite(info_cat["i_psf"])
#info_cat = info_cat[mask]
# Initialise settings for each band
band_settings_b = {
